Application Of Logistic Regression Model Research Articles

An application of logistic regression model to the study of constitutional imbalances in human chromosomal fragile sites

An application of logistic regression model to the study of constitutional imbalances in human chromosomal fragile sites

Application of logistic regression model on the spread of malaria infection in Calabar municipality (A case study of university of Calabar teaching hospital)

Background: The malaria disease is the outcome of the interaction among three elements which includes; man, mosquito and the parasite. The intensity of the disease is being regulated by the physical and socio-economic determinant in the area which interact with these elements. The physical conditions of the region determine the growth and proliferation of mosquito and parasite, while the socio-economic conditions of the people determine the distribution of mosquito and parasites. This piece of research work has been devoted to the study of vector species (Plasmodium), spatial-temporal incidence pattern of malaria i.e parasite load, physical and socio-economic determinants responsible for the spread of mosquito and parasites, control measures and risk factor assessment. The favourable explanatory variables considered in the prediction of malaria prevalence such as age group, gender, blood group and genotype presents suitable conditions to determine the validity of malaria prevalence across the metropolis which as well substantially contributed and facilitate the growth and diffusion of malarial incidence in Calabar Municipality. The data obtained was initially entered in Microsoft Excel (2016) and checked for errors after which it was exported to IBM SPSS Statistics 23 software for logistic regression analysis. The metropolis records high incidence of malaria. The predominant parasite plasmodium falciparum is considered vital for causing considerable morbidity and mortality in the area. From our analysis, it is observed that fifty nine of our patients were malaria negative and one hundred and one patients were malaria positive. The model predicted in respect to gender that seventy four percent of female population were malaria positive likewise the male gender predicted at eighty percent to be malaria positive.

Application of Logistic Regression Model in Mental Health Evaluation: A Case Study of Farmers Working Under High Voltage Transmission Line (HVTL)

The current study examined the environmental impact of High Voltage Transmission Lines (HVTL) on human health, specifically the psychological health of farmers in Pakistan. The electric and magnetic fields that emanate from HVTL may have adverse effects on human health including psychological disorders. To investigate the probability of psychological disorders among farmers working under HVTL, the current study employed a logistic regression model considering the variables of gender, age, transmission line type, work duration, and prior health conditions. The results showed that women working under HVTL are more vulnerable to psychological disorders than men. Additionally, the probability of psychological disorders increases with age of the farmers, voltage magnitude, and duration of work under HVTL. The Female farmers aged between 46 to 69 years, working under a 220 KV HVTL for up to six hours daily, were at a significantly higher risk of experiencing psychological disorders. These findings highlighted the environmental impact of HVTL on psychological health of the farmers. Moreover, it also highlighted the need for adopting measures to mitigate the risk and protect both the environment and human health.

Application of logistic regression model for hazard assessment of landslides caused by the 2012 Yiliang Ms 5.7 earthquake in Yunnan Province, China

Application of logistic regression model for hazard assessment of landslides caused by the 2012 Yiliang Ms 5.7 earthquake in Yunnan Province, China

Application of Logistic Regression Model in Prediction of Early Diabetes Across United States

This study examines a case study and impact of predicting early diabetes in United States through the application of Logistic Regression Model. After comparing the predictive ability of machine learning algorithm (Binomial Logistic Model) to diabetes, the important features that causes diabetes were also studied. We predict the test data based on the important variables and compute the prediction accuracy using the Receiver Operating Characteristic (ROC) curve and Area Under Curve (AUC). From the correlation coefficient analysis, we can deduce that, out of the 16 PIE variables, only “Itching and Delayed healing” were statistically insignificant with the target variable (class) with a value of 83% and 33% respectively while “Alopecia and Gender/Sex” has a negative correlation with the target variable (class). In addition, the Lasso Regularization method was used to penalize our logistic regression model, and it was observed that the predictor variable “sudden_weight_loss” does not appear to be statistically significant in the model and the predictor variables “Polyuria and Polydipsa” contributed most to the prediction of Class "Positive" based on their parameter values and odd ratios. Since the confidence interval of our model falls between 93% and 99%, we are 95% confident that our AUC is accurate and thus, it indicates that our fitted model can predict diabetes status correctly.

Application of Logistic Regression Model to Assess Quality Development in Organizations in Central Pomerania Region

The article focuses on the area of assessing quality development in organizations within standardized management systems. The aim of the research was to identify and assess the effectiveness of standardized management systems in organizations in Central Pomerania using a logistic regression model.

Application of Logistic Regression Model to Assess the Impact of Smartwatch on Improving Road Traffic Safety: A Driving Simulator Study

Speeding is one of the most relevant risk behaviours for serious and fatal road traffic accidents, particularly among young drivers, being the cause of approximately every third road traffic accident. Due to this background, many road traffic safety campaigns are aimed at reducing speeding among young drivers. However, the effects of campaigns aimed at complying with speed limits for young drivers have significantly fewer effects than other campaigns. For these reasons, an experimental study was conducted to examine how young drivers react to the speeding campaign, which was shown to them on a smartwatch while driving in a driving simulator. Speeding results were compared for three scenarios: no campaign, a billboard campaign and a smartwatch campaign. The experiment involved 102 participants with an average age of 21 years. The results showed that participants were six times more likely to comply with the speed limit if a campaign was shown on a smartwatch than when shown on billboards.

An observational study to assess reasons for lack of control among patients of bronchial asthma attending a tertiary care centre in India

Background: Bronchial asthma is a common chronic respiratory ailment affecting both children and adults. Lack of control among asthmatics leads to adverse outcomes and increased health care costs. Studies from India evaluating level of control and reasons for inadequate control are lacking. Objective was to assess level of control and reasons for partly/poorly controlled asthma.Methods: Consecutive patients of bronchial asthma (>8 weeks on treatment) presenting to a tertiary care centre and consenting for participation in the study were enrolled. Control was assessed used global initiative for asthma (GINA) guidelines and patients categorised into poor, partly or well control.1 Enrolled patients were assessed by a questionnaire to record their demographic details including body mass index (BMI), duration of disease, smoking status, environmental smoke exposure/biomass fuel exposure, adherence to therapy, technique of inhaled medication and anxiety/depression, co morbidities including allergic rhinitis, gastro-oesophageal reflux (GER) symptoms, obstructive sleep apnoea (OSA) and allergic bronchopulmonary aspergillosis were also assessed.Results: 150 consecutive patients, 58% females and 42% males with mean age of 40.03±14.77 years were enrolled. 19.3% of patients were well controlled while 50.7% were poorly and 30% were partly controlled. On comparing well controlled versus uncontrolled asthma (poor and partly controlled) adherence, device technique of inhaled medication, depression scores and eosinophilia showed a significant association. On application of logistic regression model, depression scores, inhaler adherence and device technique were associated with level of control.Conclusions: Asthma is partly/poorly controlled in majority of asthmatics. Psychiatric co-morbidities, adherence to therapy and correct device technique are major reasons for partly/poorly controlled asthma. Addressing these modifiable factors can improve level of control among asthmatics.

Application of logistic regression model for predicting the association of climate change resilient cultural practices with early blight of tomato (Alternaria solani) epidemics in the East Shewa, Central Ethiopia

ABSTRACT A field survey was conducted to determine the distribution and importance of early blight of tomato in East Shewa, Central Ethiopia. A total of 140 tomato fields were inspected in 4 districts (Adama, Lome, Dugda, and Bora). The associations of early blight incidence and severity with independent variables were evaluated. Disease incidence was found higher in Dugda (72.19%) and Bora (62.28%) districts. The highest mean disease severity was 31.39% in Dugda and 26.09% in Bora district, while the lowest was recorded in Adama (14.71%) district. In reduced multiple variable models, early blight percentage severity index >25% showed a high probability of association with all parameters. Logistic regression analyses of disease severity revealed that sometraits were found the most significant variables. Overall, proper weed management practices, crop rotation with non-solanaceous hosts, and other related farm practices should be carried out to reduce the impact of early blight on tomatoes.

Knowledge regarding cervical cancer and HPV vaccine among medical students: A cross-sectional study

BackgroundHealthcare workers, especially the young medical students, can play a significant role in providing the recommendations to prevent or manage cervical cancer (CC) caused by human papilloma virus (HPV). However, it is often observed that the awareness about the knowledge of CC and HPV is limited in the medical students, who are part of the front-line support for managing the patients. ObjectiveTo evaluate the knowledge, attitude, and acceptance of CC and HPV vaccine among medical students. Methodology: In this cross-sectional study, 297 medical students of the age group >18 years were included. Their knowledge regarding cervical cancer (risk factors, screening and prevention) was explored through a questionnaire. The acceptability of HPV vaccine among girls was also assessed. Descriptive statistics, chi-square test and regression method were used to analyze the study data with p < 0.05 considered as statistically significant. ResultsKnowledge regarding cervical cancer and HPV vaccine was 2.38 times more in MBBS III-year subjects than compared to MBBS II year (p > 0.0001). Proportion of subjects willing for vaccination in future was high among females (p > 0.0001). Age (p = 0.047) and education (p > 0.0001) were the only variables significant after application of logistic regression model. ConclusionThe study suggests that the overall awareness and knowledge about CC, HPV and HPV vaccination was high among college students and associated with sex, education and also family background. This study inferred that a vast majority of subjects were fairly aware of CC, but the knowledge of risk factors and prevention techniques was lacking.

Flood susceptibility modeling and hazard perception in Rwanda

Flooding is a deleterious phenomenon that induces detrimental impacts on humans, properties and environment. As a result, the knowledge of susceptible places and hazard perception is increasingly pertinent. This study mainly aims at identifying areas susceptible to flood through the application of logistic regression model using remote sensing data (RS) and Geographical Information System (GIS). A flood inventory was generated using 153 historical flood locations and a total of 10 predicting factors (elevation, slope, aspect, profile curvature, distance from rivers, distance from roads, the Normalized Difference Vegetation Index (NDVI), the Normalized Difference Soil Index (NDSI), the Topographic Wetness Index (TWI) and rainfall) were utilized. Flood points were randomly subdivided into training (75%) for model building and testing (25%) points for validation through the Area Under Curve (AUC) approach. The results indicated that NDVI and rainfall are the most influencing variables for estimating flood risk as they showed a high positive relationship with flood occurrence in the study area. Testing datasets disclosed 79.8% of prediction rate using the AUC. Moreover, the results have been linked with community perception on flood and the outcome revealed that the government is perceived as responsible for all flood mitigation measures instead of being a shared responsibility. This perception may contribute to the increase in susceptibility. The results of this study will be essential for upcoming development projects from different organizations operating in many developing countries and would assist as a baseline for flood risk reduction and management especially for Rwanda.

Evaluation of flood susceptibility mapping using logistic regression and GIS conditioning factors

This paper investigates the application of logistic regression model for flood susceptibility mapping in southern Gaza Strip areas. At first, flood inventory maps were identified using Palestinian Water Authorities data and extensive field surveys. A total of 140 flood locations were identified, of which 70% were randomly used for data training and the remaining 30% were used for data validation. In this investigation, six causing flood variables from the spatial database were prepared, which are digital elevation model (DEM), topographic slope, flow accumulation, rainfall, land use/land cover (LULC), and soil type. Then, comprehensive statistical analysis techniques including Pearson’s correlation, multicollinearity, and heteroscedasticity analyses were used, to ensure that the regression assumptions are not violated. The uniqueness of the current study is its inclusiveness of influential causing flood parameters and vigorous statistical analyses that led to accurate flood prediction. Quantitatively, the proposed model is robust with very reasonable accuracy. The prediction and success rates are 76 and 81%, respectively. The practical and unique contribution of this investigation is the generation of flood susceptibility map for the region. This is a very useful tool for the decision makers in the Gaza Strip to reduce human harm and infrastructure losses.

Analysis of the relationships between income level and iife satisfaction: Application of logistic regression model

Analysis of the relationships between income level and iife satisfaction: Application of logistic regression model

Socio-Economic Determinants of Low Birth Weight in Kenya: An Application of Logistic Regression Model

Babies born with Low-birth weight are at increased risk for serious health problems which are accompanied by disabilities and even death. The purpose of this study was to determine socio-economic factors that lead to low birth weight of children in Kenya. Data used was from Kdhs 2003 and the significant effect of socio-economic determinants on low birth weight was examined using logistic regression analysis data is categorical and continuous in nature, where predictor variables being socio-economic determinants and birth weight being dependent variable. Results indicate that out of six socio-economic factors involved in the study, four (Religion, Time Wanted Pregnancy, Marital Status and Economic Status) revealed some significant effects on the children with low birth weight. Therefore Socio-economic determinants have a significant effect on Low birth weight which suggests a strong negative associated with infant survival in Kenya independent of other risk factors. The logistic function revealed a statistically significant association between the birth weight, Religion, Time Wanted Pregnancy, Marital Status and Economic Status. Predicted probability is 11.4% low birth weight. Researcher recommends that respondents should avoid conceiving unexpectedly since it was associated with high low birth weight. Also to effectively enhance normal birth weight in Kenya, then expectant mothers should keenly focus on the socio-economic determinants by avoiding marital problems like divorce.

Application of Logistic Regression Model in Physics Education

This paper introduces a logistic regression model used to analyze multiple-choice test data of physics test. It does not involve decision making. It is a predictive model and is more akin to nonlinearity, such as fitting a polynomial to a set of data values. Brief description of the goals and algorithms of such a model is provided, together with examples illustrating their applications in physics. Priority haven been given for data interpretation rather than mathematical complexities.Key words: Logistic regression; Multiple choice test items; Classical test theory; Item response theory; Probability; EvaluationThe Himalayan Physics Vol.1, No.1, May, 2010Page: 4-9Uploaded Date: 28 July, 2011

Application of Logistic Regression Model to Graduating CGPA of University Graduates

Application of Logistic Regression Model to Graduating CGPA of University Graduates

Factores asociados al abuso y la dependencia de sustancias en adolescentes colombianos, con base en los resultados del WMH-CIDI

IntroductionGiven the magnitude of mental health problem and the recommendations of national and international institutions, it was proposed to generate knowledge about risk and protective factors for the targeting of evidence-based practice, programs for the promotion and prevention substance use disorders in adolescents. MethodAs part of the WHO-WMH studied a probabilistic sample of 1,576 Colombian adolescents from 13 to 17 years old, CIDI instrument was applied for the diagnosis of substance abuse disorder, estimated the association with etiologic factors by bivariate analysis and application of logistic regression model using the method Step Wise. ResultsIn calculating the OR adjusted with 95% confidence, the risk factors most strongly associated with substance use disorders were: a history of marijuana use, parental history of mental illness, neglect in addressing the needs of food and health and abuse among parents, the cigarette and alcohol consumption and presence of stressful life events. They behaved as protective factors: Good communication with parents, spirituality, family cohesion and the strict application of the rules. ConclusionThese results suggest that prevention programs should pay special attention to population groups that present the risk and protective factors for drug use found in this study.

Application of logistic regression model for slope instability prediction in Cuyahoga River Watershed, Ohio, USA

High incidences of slope movement are observed throughout Cuyahoga River watershed in northeast Ohio, USA. The major type of slope failure involves rotational movement in steep stream walls where erosion of the banks creates over-steepened slopes. The occurrence of landslides in the area depends on a complex interaction of natural as well as human induced factors, including: rock and soil strength, slope geometry, permeability, precipitation, presence of old landslides, proximity to streams and flood-prone areas, land use patterns, excavation of lower slopes and/or increasing the load on upper slopes, alteration of surface and subsurface drainage. These factors were used to evaluate the landslide-induced hazard in Cuyahoga River watershed using logistic regression analysis, and a landslide susceptibility map was produced in ArcGIS. The map classified land into four categories of landslide susceptibility: low, moderate, high, and very high. The susceptibility map was validated using known landslide locations within the watershed area. The landslide susceptibility map produced by the logistic regression model can be efficiently used to monitor potential landslide-related problems, and, in turn, can help to reduce hazards associated with landslides.

A study on the estimation model of suitable logging operation system using site conditions : application of logistic regression model

A study on the estimation model of suitable logging operation system using site conditions : application of logistic regression model

Logistic Discriminant Functions in Electroencephalography

Introduction. Neurofeedback treatments in clinical settings are designed to normalize abnormal quantitative EEG (QEEG) features. Typically the patient’s electrophysiological features are compared to a database in order to quantify the patient’s deviance from normative values. A clear diagnosis is crucial in deciding the most appropriate protocol for the case. Combining the initial diagnosis with the database information offers a reliable procedure for deciding the most appropriate neurofeedback protocol. Method. Logistic regression is a powerful model for performing discriminant analysis. Discriminant functions are a tool used to quantify the probability that the patient’s QEEG features are typical of either of two groups. One of the two groups is generally chosen to be normative, while the other is a homogeneous clinical group. While normative databases can detect deviation from normality, they cannot indicate if the QEEG pattern is typically observed in a particular clinical condition. Discriminant functions, however, address these types of issues. Consequently, normative databases and discriminant functions can be considered as complementary tools. With the widespread availability of software which is able to compute the maximum likelihood estimations, logistic regression has recently became a popular tool in biology. However, to date it has received little attention in electrophysiology. With this article we hope to stimulate the application of logistic regression model in this field. The logistic regression discriminant model with a single quantitative predictor is illustrated. Results. The construction of a discriminant function is presented in a step-by-step fashion, stressing methodological, practical and theoretical concerns that should be addressed in order to achieve valid and useful results. A working example illustrates, in a step-by-step fashion, how to implement the discriminant process. Discussion. The approach taken in this article aims to highlight the intuitive appeal and simplicity of this technique. It is shown that the discriminant process can easily be automated with little effort on the part of the clinician.